Remote sensing technologies for physiotherapy assessment

The paper presents a set of remote, unobtrusive sensing technologies that can be used in upper and lower limbs rehabilitation monitoring. The advantages of using sensors based on microwave Doppler radar or infrared technologies for physiotherapy assessment are discussed. These technologies allow motion sensing at distance from monitored subject, reducing thus the discomfort produced by some wearable technologies for limbs movement assessment. The microwave radar that may be easily hidden into environment by nonmetallic parts allows remote sensing of human motion, providing information on user movements characteristics and patterns. The infrared technologies - infrared LEDs from Leap-Motion, infrared laser from Kinect depth sensor, and infrared thermography can be used for different movements' parameters evaluation. Visible for users, Leap-motion and Kinect sensors assure higher accuracy on body parts movements' detection at low computation load. These technologies are commonly used for virtual reality (VR) and augmented reality (AR) scenarios, in which the user motion patterns and the muscular activity might be analyzed. Thermography can be employed to evaluate the muscular loading. Muscular activity during movements training in physiotherapy can be estimated through skin temperature measurement before and after physical training. Issues related to the considered remote sensing technologies such as VR serious game for motor rehabilitation, signal processing and experimental results associated with microwave radar, infrared sensors and thermography for physiotherapy sensing are included in the paper.info:eu-repo/semantics/acceptedVersio

MONITOREO DE SIGNOS VITALES USANDO IoT (MONITORING OF VITAL SIGNS USING IoT)

ResumenSe presenta un sistema para monitorear los signos vitales de una persona desde la Internet. El objetivo fue reportar a un servidor ubicado en la nube los signos vitales de un paciente: presión arterial, ritmo cardiaco, temperatura y frecuencia respiratoria. Fue realizado para que personal médico realice el diagnóstico del estado de salud de personas que vivan solas o en lugares apartados y brindar atención oportuna. El sistema se compone de tres módulos: el colector de signos vitales, la interfaz de comunicación inalámbrica y la interfaz de usuario. El colector de información transmite el valor de los signos vitales a la plataforma de IoT ThinkSpeak y la interfaz de usuario permite visualizar el valor de los signos. Si alguno de los signos alcanza el umbral establecido, se transmite un SMS y un mensaje de WhatsApp a un teléfono móvil. El alcance del sistema fue 48 metros al punto de acceso.Palabras Claves: Internet, presión sanguínea, ritmo cardiaco, signos vitales, WiFi. AbstractThis paper presents a system to monitor the vital signs of a person from the Internet. The objective was to report to a server located in the cloud the vital signs of a patient: blood pressure, heart rate, temperature and respiratory rate. It was made for medical personnel to diagnose the state of health of people living alone or in remote places and provide timely care. The system consists of three modules: the vital signs collector, the wireless communication interface and the user interface. The information collector transmits the value of the vital signs to the ThinkSpeak IoT platform and the user interface allows you to visualize the value of the signs. If any of the signs reaches the set threshold, an SMS and a WhatsApp message is transmitted to a mobile phone. The range of the system was 48 meters to the access point.Keywords: Blood pressure, heart rate, Internet, vital signs, WiFi

Computer-Aided Diagnosis for Early Identification of Multi-Type Dementia using Deep Neural Networks

With millions of people suffering from dementia worldwide, the global prevalence of this condition has a significant impact on the global economy. As well, its prevalence has a negative impact on patients’ lives and their caregivers’ physical and emotional states. Dementia can be developed as a result of some risk factors as well as it has many forms whose signs are sometimes similar. While there is currently no cure for dementia, effective early diagnosis is essential in managing it. Early diagnosis helps people in finding suitable therapies that reduce or even prevent further deterioration of cognitive abilities, and in taking control of their conditions and planning for the future. Furthermore, it also facilitates the research efforts to understand causes and signs of dementia. Early diagnosis is based on the classification of features extracted from three-dimensional brain images. The features have to accurately capture main dementia-related anatomical variations of brain structures, such as hippocampus size, gray and white matter tissues’ volumes, and brain volume. In recent years, numerous researchers have been seeking the development of new or improved Computer-Aided Diagnosis (CAD) technologies to accurately detect dementia. The CAD approaches aim to assist radiologists in increasing the accuracy of the diagnosis and reducing false positives. However, there is a number of limitations and open issues in the state-of-the-art, that need to be addressed. These limitations include that literature to date has focused on differentiating multi-stage of Alzheimer’s disease severity ignoring other dementia types that can be as devastating or even more. Furthermore, the high dimensionality of neuroimages, as well as the complexity of dementia biomarkers, can hinder classification performance. Moreover, the augmentation of neuroimaging analysis with contextual information has received limited attention to-date due to the discrepancies and irregularities of the various forms of data. This work focuses on addressing the need for differentiating between multiple types of dementia in early stages. The objective of this thesis is to automatically discriminate normal controls from patients with various types of dementia in early phases of the disease. This thesis proposes a novel CAD approach, integrating a stacked sparse auto-encoder (SSAE) with a two- dimensional convolutional neural network (CNN) for early identification of multiple types of dementia based on using the discriminant features, extracted from neuroimages, incorporated with the context information. By applying SSAE to intensities extracted from magnetic resonance (MR) neuroimages, SSAE can reduce the high dimensionality of neuroimages and learn changes, exploiting important discrimination features for classification. This research work also proposes to integrate features extracted from MR neuroimages with patients’ contextual information through fusing multi-classifier to enhance the early prediction of various types of dementia. The effectiveness of the proposed method is evaluated on OASIS dataset using five different relevant performance metrics, including accuracy, f1-score, sensitivity, specificity, and precision-recall curve. Across a cohort of 4000 MR neuroimages (176 × 176) as well as the contextual information, and clinical diagnosis of patients serving as the ground truth, the proposed CAD approach was shown to have an improved F-measure of 93% and an average area under Precision-Recall curve of 94%. The proposed method provides a significant improvement in classification output, resulted in high and reproducible accuracy rates of 95% with a sensitivity of 93%, and a specificity of 88%